首页|MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

MGCPN:An Efficient Deep Learning Model for Tibetan Plateau Precipitation Nowcasting Based on the IMERG Data

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  • 国家科技期刊平台
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The sparse and uneven placement of rain gauges across the Tibetan Plateau(TP)impedes the acquisition of precise,high-resolution precipitation measurements,thus challenging the reliability of forecast data.To address such a chal-lenge,we introduce a model called Multisource Generative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM)for Precipitation Nowcasting(MGCPN),which utilizes data products from the Integ-rated Multi-satellite Retrievals for global precipitation measurement(IMERG)data,offering high spatiotemporal res-olution precipitation forecasts for upcoming periods ranging from 30 to 300 min.The results of our study confirm that the implementation of the MGCPN model successfully addresses the problem of underestimating and blurring precipitation results that often arise with increasing forecast time.This issue is a common challenge in precipitation forecasting models.Furthermore,we have used multisource spatiotemporal datasets with integrated geographic ele-ments for training and prediction to improve model accuracy.The model demonstrates its competence in generating precise precipitation nowcasting with IMERG data,offering valuable support for precipitation research and forecast-ing in the TP region.The metrics results obtained from our study further emphasize the notable advantages of the MGCPN model;it outperforms the other considered models in the probability of detection(POD),critical success in-dex,Heidke Skill Score,and mean absolute error,especially showing improvements in POD by approximately 33％,19％,and 8％compared to Convolutional Gated Recurrent Unit(ConvGRU),ConvLSTM,and small Attention-UNet(SmaAt-UNet)models.

precipitation nowcastingGenerative Adversarial Network-Convolutional Long Short-Term Memory(GAN-ConvLSTM)for Precipitation Nowcasting(MGCPN)Integrated Multi-satellite Retrievals for globalprecipitation measurement(IMERG)deep learningTibetan Plateau

Mingyue LU、Zhiyu HUANG、Manzhu YU、Hui LIU、Caifen HE、Chuanwei JIN、Jingke ZHANG

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Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science & Technology,Nanjing 210044,China

Geographic Science College,Nanjing University of Information Science & Technology,Nanjing 210044,China

Department of Geography,Pennsylvania State University,University Park,PA 16802,USA

School of Remote Sensing & Geomatics Engineering,Nanjing University of Information Science & Technology,Nanjing 210044,China

Zhenhai District Meteorological Bureau,Ningbo 315200,China

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National Natural Science Foundation of ChinaNational Natural Science Foundation of China

4187128552104158

2024

10.1007/s13351-024-3211-1

气象学报(英文版)
中国气象学会

气象学报(英文版)

CSTPCD
影响因子：0.57
ISSN：0894-0525
年,卷(期)：2024.38(4)